Before proceeding, it should be noted that the predictions presented below should not be considered as valid estimations of the election results, but as part of an ongoing research. The reason they are published here is for providing evidence that the method was developed and applied before the elections and hence is not biased towards the actual results.
In our previous work we have exploited the potential of Twitter data to forecast the 2014 EU elections for three different countries. In the current research, we focus on the Greek elections, which have just begun and are expected to end at 19:00 (local time).
Since January, 9th we have been aggregating tweets written in the Greek language containing a political party’s name, its abbreviation or some common mispells. We have also aggregated opinion polls conducted during the same period. We extracted several features out of the aggregated data (Twitter-based and poll-based) and treated our task as a time-series forecasting problem.
The predictions of our method are summarised below. While several improvements to our past approach have been made (including a more appropriate sentiment analysis method and the use of Twitter-users’ weights), the training period was considerably shorter (16 days compared to 48 in the EU elections). Thus, the predictions should be read with caution. We plan to compare our model to all polls conducted during our processing time, as well as to the 19:00 Exit Polls.
| Party | Voting Share (%) | Number of Seats |
|---|---|---|
| SYRIZA | 37.16 | 151 |
| New Democracy | 28.31 | 77 |
| Golden Dawn | 6.42 | 17 |
| To Potami | 5.90 | 16 |
| KKE | 5.45 | 15 |
| PASOK | 5.10 | 14 |
| ANEL | 3.65 | 10 |
| KIDISO | 2.86 | 0 |
| Others (total) | 5.15 | — |
It should be noted that in order for a party to enter the parliament, a minimum of 3% of the total votes is needed, whie in order for a political party to form a government by its own, a minimum of 151 (out of 300) seats is needed.
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